Binary JavaScript Object Notation Research Articles

An analysis of using binary JSON versus native JSON on the example of Oracle DBMS

JSON is a popular and proven standard for specifying self-describing text files with a flexible structure. To maintain its position in the market, Oracle introduced support for JSON data in the 12c R1 version of its DBMS. This version has introduced functions for storing and managing JSON data in native form but also showed some limitations. Each new version introduced new or updated JSON functions. The 21c can store JSON data in binary form, provides more straightforward syntax and even supports JSON as a predefined data type. The paper aims to compare the performance when the underlying storage of JSON is native or binary. A data model and seven use cases were designed to demonstrate earlier and new functionalities. Additionally, experiments showed the impact of JSON data stored in native (19c and 21c) and binary form (21c) on the average execution time and costs of SQL statements.

High density genotype storage for plant breeding in the Chado schema of Breedbase.

Modern breeding programs routinely use genome-wide information for selecting individuals to advance. The large volumes of genotypic information required present a challenge for data storage and query efficiency. Major use cases require genotyping data to be linked with trait phenotyping data. In contrast to phenotyping data that are often stored in relational database schemas, next-generation genotyping data are traditionally stored in non-relational storage systems due to their extremely large scope. This study presents a novel data model implemented in Breedbase (https://breedbase.org/) for uniting relational phenotyping data and non-relational genotyping data within the open-source PostgreSQL database engine. Breedbase is an open-source, web-database designed to manage all of a breeder’s informatics needs: management of field experiments, phenotypic and genotypic data collection and storage, and statistical analyses. The genotyping data is stored in a PostgreSQL data-type known as binary JavaScript Object Notation (JSONb), where the JSON structures closely follow the Variant Call Format (VCF) data model. The Breedbase genotyping data model can handle different ploidy levels, structural variants, and any genotype encoded in VCF. JSONb is both compressed and indexed, resulting in a space and time efficient system. Furthermore, file caching maximizes data retrieval performance. Integration of all breeding data within the Chado database schema retains referential integrity that may be lost when genotyping and phenotyping data are stored in separate systems. Benchmarking demonstrates that the system is fast enough for computation of a genomic relationship matrix (GRM) and genome wide association study (GWAS) for datasets involving 1,325 diploid Zea mays, 314 triploid Musa acuminata, and 924 diploid Manihot esculenta samples genotyped with 955,690, 142,119, and 287,952 genotype-by-sequencing (GBS) markers, respectively.

Native JSON datatype support

Both RDBMS and NoSQL database vendors have added varying degrees of support for storing and processing JSON data. Some vendors store JSON directly as text while others add new JSON type systems backed by binary encoding formats. The latter option is increasingly popular as it enables richer type systems and efficient query processing. In this paper, we present our new native JSON datatype and how it is fully integrated with the Oracle Database ecosystem to transform Oracle Database into a mature platform for serving both SQL and NoSQL style access paradigms. We show how our uniquely designed Oracle Binary JSON format (OSON) is able to speed up both OLAP and OLTP workloads over JSON documents.

Geohashed Spatial Index Method for a Location-Aware WBAN Data Monitoring System Based on NoSQL

The exceptional development of electronic device technology, the miniaturization of mobile devices, and the development of telecommunication technology has made it possible to monitor human biometric data anywhere and anytime by using different types of wearable or embedded sensors. In daily life, mobile devices can collect wireless body area network (WBAN) data, and the co-collected location data is also important for disease analysis. In order to efficiently analyze WBAN data, including location information and support medical analysis services, we propose a geohash-based spatial index method for a location-aware WBAN data monitoring system on the NoSQL database system, which uses an R-tree-based global tree to organize the real-time location data of a patient and a B-tree-based local tree to manage historical data. This type of spatial index method is a support cloud-based location-aware WBAN data monitoring system. In order to evaluate the proposed method, we built a system that can support a JavaScript Object Notation (JSON) and Binary JSON (BSON) document data on mobile gateway devices. The proposed spatial index method can efficiently process location-based queries for medical signal monitoring. In order to evaluate our index method, we simulated a small system on MongoDB with our proposed index method, which is a document-based NoSQL database system, and evaluated its performance.

JSONiq: The History of a Query Language

NoSQL data stores support hierarchical, heterogeneous data such as XML, JavaScript Object Notation (JSON), and Binary JSON (BSON). A need exists for a tailor-made language for querying such hierarchical, heterogeneous datasets. JSONiq was designed from the ground up to support JSON data and run against all JSONor BSON-based data stores, turning them into full-fledged databases.